Combined Design and Control Optimization: Application to Optimal PHEV Design and Control for Multiple Objectives.

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This dissertation develops algorithms for optimal design and control solutions of dynamic systems in a computationally efficient manner. These methods are demonstrated by applying them to a Plug-in Hybrid Electric Vehicle (PHEV) powertrain’s optimal design and control. Since a PHEV draws energy from the grid it is important to consider these interactions in its optimal design and control decisions. The battery size also affects the amount of grid energy transferred to propulsion and consequently the on-road power management decisions. Thus, we develop algorithms to determine the optimal PHEV battery size and control decisions considering conditions on the electric grid. First, we develop a Dynamic Programming (DP) based algorithm for optimal on-road power management of a series PHEV. A backward looking implementation of the PHEV powertrain’s dynamic model with the DP algorithm avoided the need to interpolate the value function or enforce constraints through penalty functions, thereby a

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